Imagine a waiting list for a life-saving organ where the rules for who goes first are 20 years old and outdated. This project creates a smart AI system that acts like a high-precision triage tool to identify who is truly at the highest risk of dying. It ensures the right patients get transplants in time by using modern data instead of old formulas.
Current liver transplant allocation relies on a 20-year-old model that fails to accurately predict mortality for liver cancer (HCC) patients. This leads to inefficient organ use and high wait-list mortality rates.
An AI-based predictive algorithm for mortality stratification, professional decision-support calculators for DC and HCC, and a prototype 3rd-generation model integrating OMICS/radiomics.
If you are a medical AI developer dealing with outdated clinical scoring systems — this project developed AI-based predictive algorithms and calculators that better stratify patient mortality risk. This allows for the creation of high-precision decision-support tools for surgeons.
If you are an OSO dealing with a 15 to 30% mortality rate in low/medium donation areas — this project developed a validated predictive algorithm to replace the 20-year-old MELD model. This reduces disparities in access to transplants across Europe.
If you are a lab dealing with complex liver cancer (HCC) prognosis — this project developed a 3rd-generation exploratory model integrating OMICS and radiomics signatures. This provides a deeper biological layer for predicting patient outcomes.
Based on available project data, no pricing or cost information is provided as this is a research-funded project.
Yes, the project aims to provide tools for Organ Sharing Organizations (OSOs) to drive allocation across multiple European countries, indicating a design for large-scale institutional use.
Based on available project data, specific licensing terms are not mentioned, though the project involves a consortium of 18 partners including SMEs and research labs.
The project develops calculators and algorithms designed to be used by professionals for assistance in complex decision-making processes, likely integrating with OSO data systems.
The project period runs from 2023-11-01 to 2028-10-31, suggesting that full validation and deployment occur toward the end of this window.
The consortium is heavily weighted toward clinical and research expertise, featuring 18 partners across 9 countries. With 7 research institutes and 3 universities, the project is science-led, but the inclusion of 2 industry partners (including 1 SME) and 6 other organizations (likely OSOs) ensures that the AI tools are developed with practical implementation in mind.
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